Re: LTR original score feature

>It seems to me that the original score feature is not useful because it is not normalized across all queries and therefore cannot be used to compare relevance in different queries. I don't agree with this statement and it's not what Alessandro was suggesting ("When you put the original score toge

Re: LTR original score feature

What you're suggesting is that there's a "nonlinear relationship " between the original score (the input variable) and some measure of "relevance" (the outpu

Re: Solr LTR plugin - Training

Hi, Not sure if this is your issue or not, but the FieldQParser automatically converts multi-term arguments to phrases, so you might have to switch to the DisMaxQParser. I talk a little bit more about it here . -Michael On Thu, Nov 16, 201

From Zero to Learning to Rank in Apache Solr

Here's a tutorial I wrote that some of you all might find useful: https://github.com/airalcorn2/Solr-LTR. Feedback is welcome. Thanks, Michael A. Alcorn

How to Efficiently Extract Learning to Rank Similarity Features From Solr?

Hi, I'm trying to extract several similarity measures from Solr for use in a learning to rank model. Doing this mathematically involves taking the dot product of several different matrices, which is extremely fast for non-huge data sets (e.g., millions of documents and queries). However, to extrac

Re: Strange Behavior When Extracting Features

If anyone else is following this thread, I replied on the Jira. On Mon, Oct 16, 2017 at 4:07 AM, alessandro.benedetti wrote: > This is interesting, the EFI parameter resolution should work using the > quotes independently of the query parser. > At that point, the query parsers (both) receive a m

Re: Strange Behavior When Extracting Features

I believe I've discovered a workaround. If you use: { "store": "redhat_efi_feature_store", "name": "case_description_issue_tfidf", "class": "org.apache.solr.ltr.feature.SolrFeature", "params": { "q":"{!dismax qf=text_tfidf}${text}" } } i

Re: Parsing of rq queries in LTR

feature.SolrFeature", "params": { "q": "{!field f=issue_tfidf}${case_description}" } } you can then use single quotes to incorporate multi-term arguments as Alessandro suggested. I've added this information to the Jira. On Th

Re: Parsing of rq queries in LTR

It turns out my last comment on that Jira was mistaken. Multi-term EFI arguments still exhibit unexpected behavior. Binoy is trying to help me figure out what the issue is. I plan on updating the Jira once we've figured out the problem. On Thu, Oct 12, 2017 at 3:41 AM, alessandro.benedetti wrote:

Strange Behavior When Extracting Features

Hi all, I'm getting some extremely strange behavior when trying to extract features for a learning to rank model. The following query incorrectly says all features have zero values: http://gss-test-fusion.usersys.redhat.com:8983/solr/access/query?q=added couple of fiber channel&rq={!ltr model=red

Per Text Field Similarity Measures for Learning to Rank

Hi all, I recently prototyped a learning to rank system in Python that produced promising results, so I'm now looking into how to replicate that process in our Solr setup. For my Python implementation, I was using a number of features that were per field text comparisons, e.g.: 1. tfidf_case_t